class_docstr: A joint intent-slot model. This is framed as a model to do document
  classification model and word tagging tasks where the embedding and text
  representation layers are shared for both tasks.
  
  The supported representation layers are based on bidirectional LSTM or CNN.
  
  It can be instantiated just like any other :class:`~Model`.
  
  This is in the new data handling design involving tensorizers; that is the
  difference between this and JointModel
code: def arrange_targets(self, tensor_dict):
      intent_tensor = tensor_dict["doc_labels"]
      slot_tensor = tensor_dict["word_labels"]
      return intent_tensor, slot_tensor
  
imports: from typing import Optional, Union
  import pytext.utils.cuda
  import torch
  from pytext.common.constants import SpecialTokens
  from pytext.data.tensorizers import FloatTensorizer, LabelTensorizer, SlotLabelTensorizer, TokenTensorizer
  from pytext.exporters.exporter import ModelExporter
  from pytext.models.embeddings import WordEmbedding
  from pytext.models.model import Model
  from pytext.models.module import create_module
  from pytext.models.representations.pass_through import PassThroughRepresentation
  from pytext.utils.usage import log_class_usage
  from .decoders import IntentSlotModelDecoder
  from .embeddings import EmbeddingList
  from .output_layers.intent_slot_output_layer import IntentSlotOutputLayer
  from .representations.bilstm_doc_slot_attention import BiLSTMDocSlotAttention
  from .representations.jointcnn_rep import JointCNNRepresentation, SharedCNNRepresentation
label_desc: Use image node_id 5 for calling the IntentSlotModel obj's underlying member method code with example usage: obj.arrange_targets(tensor_dict) and returns: intent_tensor,  slot_tensor